The present invention relates in general to the field of process control, and in particular to a new and useful arrangement which utilizes parameter estimation, different types of parameter conversion and gain scheduling to produce a self-tuning control for a process.
Process performance can be improved by utilizing self-tuning control theory. This improvement can be substantial for processes with unknown or changing dynamics and processes that exhibit non-linear behavior. Self-tuning control can also improve the performance of simple process control loops since periodic tuning of these loops is often neglected. See Kalman, R. E., "Design of a Self-Optimizing Control System", AM. Soc. Mech. Engr. Trans., 80, 1958; Astrom, K. J. and B. Wittenmark, "On Self-Tuning Regulators" Automatica, 9, 1973; and Clarke, D. W. and P. Gawthrop, "Self-Tuning Controller", Proc. IEE, 122, 1975.
In spite of potential advantages of self-tuning control, this theory has had little impact in practice. Many practical difficulties are associated with self-tuning controls which are comprehensively discussed in the first two articles listed above. Problem areas include start-up, long term operation, unknown or varying process time delays and high order of rapidly changing process dynamics. See Wittenmark, B. and K. Astrom, "Practical issues in the Implementation of Self-Tuning Control", Automatica, 20, 1984.
Also, the number of parameters required to specified current self-tuning control generally exceeds the two or three parameters necessary for conventional PID control. While a significant amount of research addressed these problems during the past decade many problem areas still exist. Until now, self-tuning control theory was not suitable for general application to industrial control problems.
Initially, self-tuning control theory focused on implicit algorithms. These algorithms allow the direct estimation of controller parameters, but relied heavily on a model of the process to be controlled. See Astrom, K. L. Introduction to Stochastic Control Theory, Academic Press, 1970. Recent work, however, focused on explicit algorithms.
These algorithms estimate parameters by a discrete process model and subsequently calculate controller parameters. See Fortesque, T. R., L. Kershenbaum and B. Ydstie, "Implementation of Self-Tuning Regulators with Variable Forgetting Factors", Automatica, 17, 1981; Ydstie, B. E., "Extended Horizon Adaptive Control", IFAC World Congress, Budapest, 1984; and Leal, R. L. and G. Goodwin, "A Globally Convergent Adaptive Pole Placement Algorithm Without a Persistency of Excitation Requirement", Proc. CDC, 1984. While explicit algorithms require more calculations than implicit algorithms, these are less dependent on model structure and are, therefore, more suitable for general purpose application.
A self-tuning control system which relies on a variable variable forgetting factor is known from Fortesque, T. R., "Work on Astrom's Self-Tuning Regulator", Dept. of Chem. Engr. Report, Imperial College, London, 1977. This article addresses several of the practical difficulties associated with self-tuning control.
Self-tuning control systems also utilize a feed forward index or control. Feed forward control is of two types: steady state and dynamic. Parameters transferring the feedforward index to a specific control action or function f(x,t), as shown in FIG. 4 herein. Classical gain scheduling is shown in FIG. 2 herein. This is a form of adaptive control that uses apriori process information to enhance control system performance. Through this mechanism, a control engineer can incorporate process design data and operating experience into the control system. Inclusion of this knowledge diminishes the impact of process changes and disturbances on controller performance. Gain Scheduling uses a fixed equation or gain schedule to relate a measured index variable to desirable tuning parameters. The gain scheduling updates controller tuning parameters.
U.S. Pat. No. 4,563,735 to Huroi et al. discloses an adaptive feedforward control system for adapting feedforward coefficients to a variable disturbance in a steady state. The feedforward coefficient method is adapted however, only at individual operating points. There is no teaching that the data should be collected over the entire operating range of the disturbance and fitted into a correction polynomial to be added to the feedforward signal.
U.S. Pat. No. 4,349,869 to Prett et al discloses a feedforward optimization system for continuously recalculating feedforward responses using a least-squares regression algorithm. This patent also teaches the use of a limiting mechanism for allowing the feedforward correction only in a steady state and providing an upper limit on the feedforward correction for stability purposes. This reference also relies on a minimization technique which is based on an internal model of the process. No provision for updating the feedforward model is provided.
An article by one of the coinventors of the present invention which post dates the present invention is Lane, J. D., Description of a Modular Self-Tuning Control System", Proc. ACC, 1986. This article discloses how heuristics can be included in a parameter estimation function. Also see Parish, J. R., "The Use of Model Uncertaintly in Control System Design with Application to a Laboratory Heat Exchange Process", M. S. Thesis, Case Western Reserve University, Cleveland, 1982, which describes a parameter estimation algorithm with a first order internal model controller.